The ‘Smart Handshake’ — to seamlessly identify the participants + 9 ideas for apps and startups from my personal backlog
1. The ‘Smart Handshake’
Did you ever have difficulties remembering names after a business or social handshake? If yes, check this seamless solution!
Imagine this scenario: Two persons handshake in a business environment and, with no further interaction or activity, both persons receive a notification about each other, with name, photo, location, context and link to his/her LinkedIn or other social profile!
An app using the accelerometer, gyroscope and motion detection capabilities to identify the ‘universal’ pattern of a handshake
A business handshake between two persons who have installed the app in their smartphone or wearable, will trigger the following sequence of events:
- The app uses the accelerometer and related tech to identify the ‘universal’ movement of hands during a typical business handshake: based on patterns extracted from a vast volume of handshake events — movement data, the system is able to identify the gesture based on acceleration, speed, duration and the movement of the hand.
- When the handshake is identified with confidence, it is logged in the data store — independently for each of the users. The handshake event also contains the timestamp and location information.
- The system searches for other handshakes happened at the same time and location; the closest one in time and location refers to the other person of the handshake event.
- At this point both persons involved in the handshake, are identified. The app can lookup their social network profile links — for instance, LinkedIn or Facebook. Each user receives information about the other’s person identity and social media profile.
- As users continue to meet people, the app maintains the history of the new persons met (names, photos, social network profiles) and — under sufficient permissions — can automatically invite or follow the other user on the default social network.
2. DIGITAL annotations on PHYSICAL books
You are reading a book — a physical one; it might be a novel, a technical or text book. You are at an important point/ paragraph where you need help or you need to comment on/ with a question or explanation.
Imagine the following scenario:
- You use your smartphone to scan the paragraph or phrase of interest — the physical book
- The app performs OCR to extract the text from the paragraph/phrase/ page you just scan
- The app triggers a full-text-search against a large database of books — could be a service call such as Google’s Book API or similar.
- The app receives the response from the API — including the identifier of the book and the positioning — a reference to the paragraph and page.
- The app retrieves user-generated-content and metadata about the specific paragraph/phrase/page of the identified book.
- The summarized user generated content is then presented to the user via the app — possibly in an Augmented Reality mode and/ or with voice support.
- The user can use voice, via the app, to append his/her own private or public comments on the identified paragraph of the book.
Full history is maintained for the user and the book — available also via classic search experience.
3. A self-organizing ‘Do Not Disturb’ mode
Ever been in theater, cinema or other noise-sensitive social situation where sounds from mobile notifications can spoil the moment?
The common sense in such a situation is to set the mobile in silent or ‘Do not disturb’ mode. Although obvious, this is not the case for everybody: there are always those few who either by mistake or disrespectfully skip this.
What if there was a way for the audience to seamlessly self-organize?
‘The system’ could identify the situation as requiring ‘silent mode’ and notify the members of the audience to silent their mobiles (those who haven’t already); Or, in a more aggressive scenario, automatically set the phones in to ‘Do not Disturb’ mode
Mobile devices automatically enter silent mode when users join special social arrangements (a concert, a lecture etc.). This could happen seamlessly with no controlling system or particular rules: Assuming a number of people is at a particular place — within a specific radius and possibly around a particular known location; each time a mobile device is set to ‘silent mode’ by a user, an event is triggered which sends location and mode data into a centralized data store; this database allows the identification of ‘concurrent’ transitions to ‘silent mode’ within the same radius.
Multiple human-originated transitions to ‘silent mode’ which are time-aligned and within the same radius, indicate a self-adjusting behavior (people set their mobile phones to ‘silent mode’ at the same time and possibly for the same reason)
If this behavior is significant (as a percentage of the audience — more than x% of the people identified in the same radius and time frame) there is a clear signal that the particular situation (people arrangement+point in time+ location) is requiring mobile devices in silent mode. Assuming that this behavior follows particular patterns — like specific days of the week, months, time-slots within the day, size of audience, time-frame length etc. — the system can safely identify this location and time arrangement as ‘sensitive to noise’. Read more here
+7 ideas on music, news, messaging and more
— follow the link or click on the image: